Numerous articles argue filter bubbles do exist. But there is almost the same number claiming filter bubbles are nothing but a bad dream. How do we know what the truth is?
A study conducted by Flaxman et al. examined the web browsing patterns of 50,000 US internet users over three months. It showed that articles found through social media are associated with a much higher rate of ideological segregation than stories found through other means. This higher rate means that users who typically get their news through social media are more likely to be shown articles relating to their ideological stance. The study also found that individuals who read online news tend to consume more news that is in line with a particular ideology. For example, it suggests those who regularly read partisan news rarely read articles from a different political bias.
An issue with this study is that it only considered the political bias of the news outlet as opposed to the individual articles. The article also suggests that the stories found through social media tend to have the most impact which could contribute to the skew in data. Despite this, there is still evidence to show filter bubbles exist in this study.
The previously mentioned study by Dylko et al. strongly suggests the existence of filter bubbles. Results from the experiment show that modern technology may have ‘unintended and detrimental societal effects’. Particular emphasis was given to pre-selected personalisation as this led to more biased exposure than the self-selected results.
However, a limitation of this study is that it only used college students. Using students may cause issues as they often encounter and are encouraged to encounter diverse opinions and arguments. The authors note that if they were to replicate this study with a sample from the general population, the results might show increased evidence for filter bubbles.
Nikolov et al. conducted one of the first pieces of research on filter bubbles through a longitudinal study using data from 2006-2010. This article examined the diversity of information available to over 100,000 people based on each person’s online behaviour. By investigating the internet usage of the participants, the research findings suggest that filter bubbles exist at the individual level. However, this study has limitations, as it only used data from within the Indiana University network.
While the amount of data used was significant, it is hard to ascertain how diverse the participant sample was and whether it is representative of the general population. The period of the analysed data could also be an issue. Social media platforms and use has changed drastically since 2010, which could impact the reliability of this study in today’s society.
Overall, the arguments for the existence of filter bubbles are convincing, but there are two sides to every story.
Flaxman, S., Goel, S., & Rao, J. M. 2016, ‘Filter bubbles, echo chambers, and online news consumption’, Public Opinion Quarterly, vol. 80, Special Issue, pp. 298–320.
Dylko, I., Dolgov, I., Hoffman, W., Eckhart, N., Molina, M., & Aaziz, O. 2017, ‘The dark side of technology: An experimental investigation of the influence of customizability technology on online political selective exposure’, Computers in Human Behaviour, vol. 73, pp. 181-190.
Nikolov, D., Oliveira, D. F. M., Flammini, A. & Menczer, F. 2015, ‘Measuring online social bubbles’, PeerJ Computer Science, vol. 1, no. 38, pp. 1-14.